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Mobile technologies are used in increasingly diverse and challenging environments. With the predominantly visual nature of mobile devices, Situational Visual Impairments (SVIs) are a growing concern. However, fundamental knowledge is lacking about the causes of SVIs, how people deal with SVIs, and whether their solutions are effective. To address this, we first conducted a convenience-sampled online questionnaire with 174 participants, and identified many causes and (ineffective) solutions. To firmly ground our initial results, we then conducted a two-week ecological momentary assessment with 24 participants, balanced by age and gender across Australia and Scotland. We confirmed that SVIs are experienced often and during typical mobile tasks, and can be very frustrating. We identify a range of factors causing SVIs, discuss mobile design implications, and introduce an SVI Context Model rooted in empirical evidence. The contributions in this paper will support the development of new effective SVI solutions.

Mobile devices are a substantial part of our lives, supporting communication, work, and play. However, situational visual impairments (SVIs) can make completing tasks a challenge (e.g., browsing online in bright sunlight) and poorly designed content can cause or exacerbate SVIs. We surveyed 43 mobile content designers and ran four follow-on interviews to understand what designers currently do regarding SVIs, what resources they know of, and what is required to best support them in designing to reduce SVIs. Our findings highlight key similarities and differences between accessibility and designing to reduce SVIs. Our participants requested improved guidelines, education, and digital design tools for SVIs. To accommodate the growing number of people affected by SVIs and improve the inclusion of accessibility in design, we introduce recommendations that leverage the overlap between accessibility and SVIs to minimise the effort required in extending current design processes.

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Colour can convey a mood or elicit a particular emotion and, in terms of web design, colour can influence attitudes, perceptions, and behaviours. However, many websites demonstrate inaccessible colour choices. Numerous online colour palette design tools only focus on assisting designers with either the aesthetics or accessibility of colours. With a user-centered design approach, we developed the Accessible Colour Evaluator (ACE, daprlab.com/ace) which enhances web developers' and designers' ability to balance aesthetic and accessibility constraints. We distributed an online questionnaire to 28 web developers and designers to understand their attitudes and utilisation of accessibility guidelines, as well as to gather initial design requirements for ACE. With this information, we created three low-fidelity paper prototypes that were used to create two high-fidelity prototypes. The high-fidelity prototypes were discussed with 4 web developers and designers during a design workshop, and their feedback was used to develop the final version of ACE. A comparative evaluation of ACE and three existing alternative tools was conducted with 10 new web developers and designers. All participants were able to complete a colour palette design task when using ACE and identified ACE as their most preferred tool. The mean scores for the six TLX measures show ACE as providing the best performance and causing the lowest frustration. Finally, we conducted a small focus group with 3 web developers and designers to gather qualitative feedback about ACE. Participants identified a number of ACE's strengths and made suggestions for future extensions and improvements. CCS Concepts: r Human-centered computing → Accessibility systems and tools;

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Situationally-induced impairments and disabilities (SIIDs) make it difficult for users of interactive computing systems to perform tasks due to context (e.g., listening to a phone call when in a noisy crowd) rather than a result of a congenital or acquired impairment (e.g., hearing damage). SIIDs are a great concern when considering the ubiquitousness of technology in a wide range of contexts. Considering our daily reliance on technology, and mobile technology in particular, it is increasingly important that we fully understand and model how SIIDs occur. Similarly, we must identify appropriate methods for sensing and adapting technology to reduce the effects of SIIDs. In this workshop, we will bring together researchers working on understanding, sensing, modelling, and adapting technologies to ameliorate the effects of SIIDs. This workshop will provide a venue to identify existing research gaps, new directions for future research, and opportunities for future collaboration. Submission instructionsWorkshop papers are limited to a MAXIMUM of 8 pages (incl. references

Social media platforms are deeply ingrained in society, and they offer many different spaces for people to engage with others. Unfortunately, accessibility barriers prevent people with disabilities from fully participating in these spaces. Social media users commonly post inaccessible media, including videos without captions (which are important for people who are Deaf or Hard of Hearing) and images without alternative text (descriptions read aloud by screen readers for people who are blind). Users with motor impairments must find workarounds to deal with the complex user interfaces of these platforms, and users with cognitive disabilities may face barriers to composing and sharing information. We invited accessibility researchers, industry practitioners, and end-users with disabilities to come together at the Computer-Supported Cooperative Work conference (CSCW 2019) to discuss challenges and solutions for improving social media accessibility. Over the course of a day that included two panels and breakout sessions, the workshop attendees outlined four critical future research directions to progress on the path to accessible social media: tooling to support disabled people authoring content, developing more accessible formats/tools for new forms of interaction (e.g, Augmented and Mixed Reality), using communities to distribute accessibility labor, and ensuring machine learning systems are built on representative datasets for disability use-cases.